Modern statistical analysis often involves massive datasets and sophisticated models, which makes the corresponding statistical computation extremely challenging. For example, the well-known ImageNet dataset, contains more than one million photos and takes up approximately 150GB space on a hard drive [1]}. Obviously, most computers available to researchers cannot easily load such an enormous dataset as a whole into its memory. Then, how to process these massive datasets efficiently with statistical guarantees becomes a problem of great interest. To address this issue, it is in urgent need to develop novel scalable algorithms for statistical analysis.
